Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, their ST patterns can vary greatly in difficulties for modeling, owning to the heterogeneous nature of ST data. We argue that unveiling the nodes to the model in a meaningful order, from easy to complex, can provide performance improvements over traditional training procedure. The idea has its root in Curriculum Learning which suggests in the early stage of training models can be sensitive to noise and difficult samples. In this paper, we propose ST-Curriculum Dropout, a novel and easy-to-implement strategy for spatial-temporal graph modeling. Specifically, we evaluate the learning difficulty of each node in high-level feature space and drop those difficult ones out to ensure the model only needs to handle fundamental ST relations at the beginning, before gradually moving to hard ones. Our strategy can be applied to any canonical deep learning architecture without extra trainable parameters, and extensive experiments on a wide range of datasets are conducted to illustrate that, by controlling the difficulty level of ST relations as the training progresses, the model is able to capture better representation of the data and thus yields better generalization.

深度学习已被广泛应用于频划分双工（FDD）中的通道状态信息（CSI）反馈，大量多输入多输出（MIMO）系统。对于反馈模型的典型监督培训，几乎无法满足大量特定于任务标记的数据的要求，并且在多种情况下，模型的巨大培训成本和存储使用是用于模型应用的障碍。在这封信中，提出了一种基于多任务学习的方法，以提高反馈网络的可行性。进一步提出了编码者共享的反馈体系结构和相应的培训计划，以促进实施多任务学习方法。实验结果表明，提出的多任务学习方法可以实现全面的反馈绩效，而反馈模型的培训成本和存储使用情况大大降低。

公共数据集是商业AI软件的关键驱动程序之一。使用公共可用数据集（特别是商业目的）由DataSet许可证管理。这些数据集许可证概述权利人有权获得给定数据集的权利以及必须履行必须履行诸如违反许可违规行为的权利的义务。但是，与标准化开源软件（OSS）许可不同，现有数据集许可证以临时方式定义，并不明确概述与其使用相关的权利和义务。这使得检查潜在的许可合规性违规。此外，公共数据集可以托管在多个位置，并从多个数据源创建，每个数据源可以具有不同的许可。因此，不能使用现有的检查OSS许可合规性的方法。在本文中，如果要用于建立商业AI软件，则提出了一种新的方法来评估潜在的许可合规性违规行为，如果要用于建立商业AI软件。我们在Huawei的两个产品组上进行了方法，常用的公共数据集进行了试验。我们的研究结果表明，如果它们用于商业目的，这6个学习的数据集中有5个违规风险。因此，我们为AI工程师提供了如何更好地评估公开可用数据集以获得许可合规性违规的建议。

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

A storyboard is a roadmap for video creation which consists of shot-by-shot images to visualize key plots in a text synopsis. Creating video storyboards however remains challenging which not only requires association between high-level texts and images, but also demands for long-term reasoning to make transitions smooth across shots. In this paper, we propose a new task called Text synopsis to Video Storyboard (TeViS) which aims to retrieve an ordered sequence of images to visualize the text synopsis. We construct a MovieNet-TeViS benchmark based on the public MovieNet dataset. It contains 10K text synopses each paired with keyframes that are manually selected from corresponding movies by considering both relevance and cinematic coherence. We also present an encoder-decoder baseline for the task. The model uses a pretrained vision-and-language model to improve high-level text-image matching. To improve coherence in long-term shots, we further propose to pre-train the decoder on large-scale movie frames without text. Experimental results demonstrate that our proposed model significantly outperforms other models to create text-relevant and coherent storyboards. Nevertheless, there is still a large gap compared to human performance suggesting room for promising future work.

With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an indispensable component for predictive and trustworthy decision-making. Thus, it is critical to explain why graph neural network (GNN) makes particular predictions for them to be believed in many applications. Some GNNs explainers have been proposed recently. However, they lack to generate accurate and real explanations. To mitigate these limitations, we propose GANExplainer, based on Generative Adversarial Network (GAN) architecture. GANExplainer is composed of a generator to create explanations and a discriminator to assist with the Generator development. We investigate the explanation accuracy of our models by comparing the performance of GANExplainer with other state-of-the-art methods. Our empirical results on synthetic datasets indicate that GANExplainer improves explanation accuracy by up to 35\% compared to its alternatives.

变压器验证引起了机器学习研究和行业的越来越多的关注。它正式验证了变压器对对抗性攻击的鲁棒性，例如用同义词交换单词。但是，由于以中线为中心的计算，变压器验证的性能仍然不令人满意，这与标准神经网络有显着差异。在本文中，我们提出了信仰，这是用于GPU的变压器验证的有效框架。我们首先提出一个语义意识的计算图转换，以识别语义信息，例如变压器验证中的结合计算。我们利用此类语义信息，以在计算图级别启用有效的内核融合。其次，我们提出了一个验证专门的内核手工艺品，以有效地将变压器验证映射到现代GPU。该手工艺者利用了一组GPU硬件支持，以加速通常是内存密集型的验证专业操作。第三，我们提出了一个专家指导的自动调整，以纳入有关GPU后端的专家知识，以促进大型搜索空间探索。广泛的评估表明，Faith在最先进的框架上实现了$ 2.1 \ times $至$ 3.4 \ times $（$ 2.6 \ times $）的加速。

大型语言模型（LLM）从人类的指示中解开了任务计划的新功能。但是，事先尝试将LLMS应用于现实世界的机器人任务受到周围场景中缺乏接地的限制。在本文中，我们开发了NLMAP，这是一个开放式摄影和可查询场景表示，以解决此问题。 NLMAP是一个框架，可以将上下文信息收集到LLM计划者中，从而在生成上下文条件条件计划之前，可以在场景中查看和查询可用的对象。 NLMAP首先使用视觉语言模型（VLM）建立自然语言可查询场景表示。基于LLM的对象建议模块解析指令并提出涉及的对象，以查询场景表示以获取对象可用性和位置。然后，LLM规划师计划提供有关场景的此类信息。 NLMAP允许机器人在没有固定的对象列表或可执行选项的情况下操作，从而使真实的机器人操作无法通过以前的方法实现。项目网站：https：//nlmap-saycan.github.io

图形神经网络（GNN）的输入图的大小不断增加，突显了使用多GPU平台的需求。但是，由于计算不平衡和效率较低的通信，现有的多GPU GNN解决方案遭受了劣质性能。为此，我们提出了MGG，这是一种新型的系统设计，可以通过以GPU为中心的软件管道在多GPU平台上加速GNN。 MGG探讨了通过细粒度计算通信管道中隐藏GNN工作负载中远程内存访问延迟的潜力。具体而言，MGG引入了管​​道感知工作负载管理策略和混合数据布局设计，以促进通信局限性重叠。 MGG实现以优化的管道为中心的内核。它包括工作负载交织和基于经经的映射，以进行有效的GPU内核操作管道和专门的内存设计以及优化，以更好地数据访问性能。此外，MGG还结合了轻巧的分析建模和优化启发式方法，以动态提高运行时不同设置的GNN执行性能。全面的实验表明，MGG在各种GNN设置上的最先进的多GPU系统要比最先进的多GPU系统：平均比具有统一虚拟内存设计的多GPU系统快3.65倍，平均比DGCL框架快7.38倍。

自我模型是一种过程，例如动物或机器等代理商学会创建自己动态的预测模型。一旦被捕获，这种自模型就可以允许代理使用自我模型在内部计划和评估各种潜在行为，而不是使用昂贵的物理实验。在这里，我们量化了这种自模型对机器人的复杂性的好处。我们发现与直接学习基线相比，机器人拥有的自由度数量与自模型的附加值之间的R2 = 0.90相关性。这一结果可能有助于激发日益复杂的机器人系统中的自我建模，并阐明动物和人类自我模型的起源，并最终自我意识。